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#!/usr/bin/env python3
r'''Tests the mrcal optimization callback function
This is a regression test. It simply checks that the stored values are what's
output. If anything changes, this test barfs. Any changes in the internals of
the C code will trigger a failure here. If we see a failure without significant
changes though, that's a bug that should be tracked down.
To accept the current code as "right", set store_current_output_as_reference to
True, and run this script. That updates the golden reference data so that
subsequent runs of the test pass
'''
# Set this to True to store the current values as the "true" values. Leave as
# False to run the test. Leave as False in the repo
store_current_output_as_reference = False
import sys
import numpy as np
import numpysane as nps
import os
testdir = os.path.dirname(os.path.realpath(__file__))
# I import the LOCAL mrcal since that's what I'm testing
sys.path[:0] = f"{testdir}/..",
import mrcal
import testutils
from functools import reduce
def linspace_shaped(*shape):
product = reduce( lambda x,y: x*y, shape)
return np.linspace(0,1,product).reshape(*shape)
# I read the synthetic-data observations. These have 3 frames with 2 cameras
# each. I want to make things uneven, so I make the first two frames have only 1
# camera each
observations, indices_frame_camera, paths = \
mrcal.compute_chessboard_corners(10, 10,
globs_per_camera = ('frame*-cam0.xxx','frame*-cam1.xxx'),
corners_cache_vnl = f"{testdir}/data/synthetic-board-observations.vnl")
indices_frame_camintrinsics_camextrinsics = np.zeros((len(indices_frame_camera), 3), dtype=indices_frame_camera.dtype)
indices_frame_camintrinsics_camextrinsics[:, :2] = indices_frame_camera
indices_frame_camintrinsics_camextrinsics[:, 2] = indices_frame_camintrinsics_camextrinsics[:, 1]-1
i = (1,2,4,5)
observations = observations [i, ...]
indices_frame_camintrinsics_camextrinsics = indices_frame_camintrinsics_camextrinsics[i, ...]
paths = [paths[_] for _ in i]
# reference models
models = [ mrcal.cameramodel(m) for m in ( f"{testdir}/data/cam0.opencv8.cameramodel",
f"{testdir}/data/cam1.opencv8.cameramodel",) ]
lensmodel = models[0].intrinsics()[0]
intrinsics_data = nps.cat(models[0].intrinsics()[1],
models[1].intrinsics()[1])
rt_cam_ref = mrcal.compose_rt( models[1].rt_cam_ref(),
models[0].rt_ref_cam() )
imagersizes = nps.cat(models[0].imagersize(),
models[1].imagersize())
# I now have the "right" camera parameters. I don't have the frames or points,
# but it's fine to just make them up. This is a regression test.
rt_ref_frame = linspace_shaped(3,6)
rt_ref_frame[:,5] += 5 # push them back
indices_point_camintrinsics_camextrinsics = \
np.array(((0,1,-1),
(1,0,-1),
(1,1, 0),
(2,0,-1),
(2,1, 0)),
dtype = np.int32)
points = 10. + 2.*linspace_shaped(3,3)
observations_point_xy = 1000. + 500. * linspace_shaped(5,2)
observations_point_weights = np.array((0.9, 0.8, 0.9, 1.3, 1.8))
observations_point = \
nps.glue(observations_point_xy,
nps.transpose(observations_point_weights),
axis = -1)
all_test_kwargs = ( dict(do_optimize_intrinsics_core = False,
do_optimize_intrinsics_distortions= True,
do_optimize_extrinsics = False,
do_optimize_frames = False,
do_optimize_calobject_warp = False,
do_apply_regularization = True),
dict(do_optimize_intrinsics_core = True,
do_optimize_intrinsics_distortions= False,
do_optimize_extrinsics = False,
do_optimize_frames = False,
do_optimize_calobject_warp = False,
do_apply_regularization = True),
dict(do_optimize_intrinsics_core = False,
do_optimize_intrinsics_distortions= False,
do_optimize_extrinsics = False,
do_optimize_frames = True,
do_optimize_calobject_warp = False,
do_apply_regularization = True),
dict(do_optimize_intrinsics_core = True,
do_optimize_intrinsics_distortions= True,
do_optimize_extrinsics = False,
do_optimize_frames = True,
do_optimize_calobject_warp = False,
do_apply_regularization = True),
dict(do_optimize_intrinsics_core = True,
do_optimize_intrinsics_distortions= True,
do_optimize_extrinsics = True,
do_optimize_frames = True,
do_optimize_calobject_warp = True,
do_apply_regularization = False),
dict(do_optimize_intrinsics_core = True,
do_optimize_intrinsics_distortions= True,
do_optimize_extrinsics = True,
do_optimize_frames = True,
do_optimize_calobject_warp = True,
do_apply_regularization = False,
outlier_indices = np.array((1,2), dtype=np.int32)))
itest = 0
for kwargs in all_test_kwargs:
observations_copy = observations.copy()
if 'outlier_indices' in kwargs:
# mark the requested outliers, and delete the old way of specifying
# these
for i in kwargs['outlier_indices']:
nps.clump(observations_copy, n=3)[i,2] = -1.
del kwargs['outlier_indices']
optimization_inputs = \
dict( intrinsics = intrinsics_data,
rt_cam_ref = nps.atleast_dims(rt_cam_ref, -2),
rt_ref_frame = rt_ref_frame,
points = points,
observations_board = observations_copy,
indices_frame_camintrinsics_camextrinsics = indices_frame_camintrinsics_camextrinsics,
observations_point = observations_point,
indices_point_camintrinsics_camextrinsics = indices_point_camintrinsics_camextrinsics,
lensmodel = lensmodel,
calobject_warp = np.array((1e-3, 2e-3)),
imagersizes = imagersizes,
calibration_object_spacing = 0.1,
point_min_range = 1.0,
point_max_range = 1000.0,
verbose = False,
**kwargs )
x,J = mrcal.optimizer_callback( **optimization_inputs )[1:3]
J = J.toarray()
# let's make sure that pack and unpack work correctly
J2 = J.copy()
mrcal.pack_state( J2, **optimization_inputs)
mrcal.unpack_state( J2, **optimization_inputs)
testutils.confirm_equal( J2, J, msg="unpack(pack(J)) = J")
J2 = J.copy()
mrcal.unpack_state( J2, **optimization_inputs)
mrcal.pack_state( J2, **optimization_inputs)
testutils.confirm_equal( J2, J, msg="pack(unpack(J)) = J")
# I compare full-state J so that I can change SCALE_... without breaking the
# test
mrcal.pack_state(J, **optimization_inputs)
if store_current_output_as_reference:
np.save(f"{testdir}/data/test-optimizer-callback-ref-x-{itest}.npy", x)
np.save(f"{testdir}/data/test-optimizer-callback-ref-J-{itest}.npy", J)
else:
x_ref = np.load(f"{testdir}/data/test-optimizer-callback-ref-x-{itest}.npy")
J_ref = np.load(f"{testdir}/data/test-optimizer-callback-ref-J-{itest}.npy")
testutils.confirm_equal(x, x_ref, msg = f"comparing x for case {itest}")
testutils.confirm_equal(J, J_ref, msg = f"comparing J for case {itest}")
itest += 1
testutils.finish()
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